Job Title

About the RoleWe are seeking a Python Developer with strong backend engineering expertise and hands-on exposure to Generative AI, Machine Learning, and Deep Learning to design, build, and scale AI-driven applications.The role involves developing production-grade AI solutions leveraging Large Language Models (LLMs), deep learning models, and cloud AI services across cloud or on-premise environments.You will be responsible for building high-performance backend services, integrating advanced AI/ML models, and enabling scalable API-driven platforms.The ideal candidate should have experience in building LLM-powered systems, implementing Agentic AI workflows,and applying AI-first approaches to solve business problems.You will work closely with cross-functional teams to deliver reliable, scalable, and secure AI solutions integrated into enterprise systems.Key ResponsibilitiesDesign, develop, and integrate LLM-based solutions (e.g., OpenAI GPT, LLaMA, HuggingFace models) into enterprise products and workflowsImplement Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, chunking strategies, and fine-tuning for business use casesDevelop APIs and integration layers to seamlessly connect AI models with frontend and backend systemsBuild and maintain scalable backend applications using Python with microservices architectureDesign and implement RESTful APIs using frameworks such as FastAPI (mandatory), Flask, or DjangoDevelop Agentic AI workflows including multi-agent coordination, tool/function calling, memory handling, and workflow orchestrationIntegrate AI models into applications using APIs and ensure secure and efficient communication across systemsCollaborate effectively with frontend (Flutter) and backend (Node.js/Python) teams for smooth AI feature deploymentTest, debug, and manage API integrations using tools like cURL and other debugging mechanismsBuild and deploy AI services on cloud platforms using AWS services such as Lambda, S3, API Gateway, EC2, ECS/EKS, DynamoDB, and RDSLeverage Amazon Bedrock and SageMaker for model deployment, orchestration, and scalingDevelop and integrate machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, and scikit-learnWork on NLP, classification, regression, clustering, anomaly detection, and time-series modeling problemsBuild scalable data pipelines for data processing, training, validation, and inferenceEnsure systems are secure, scalable, cost-optimized, and production-ready with proper monitoring and observabilityImplement DevOps and MLOps best practices including CI/CD, model versioning, logging, and performance trackingCollaborate with product teams and stakeholders to translate business requirements into AI-driven solutionsContribute to architecture design, innovation, and continuous improvement of AI platformsRequired Technical Skills:LLM & AI Integration (Mandatory – Hands-on)Strong hands-on experience working with LLMs and Generative AI systemsExperience integrating LLMs such as OpenAI GPT, LLaMA, HuggingFace models into real-world applicationsExperience with frameworks such as LangChain, LlamaIndex, LangGraph, ADK, or similarHands-on experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, or OpenSearchProven ability to build and deploy RAG pipelines, embeddings-based retrieval systems, and prompt engineering workflowsExperience integrating AI models via APIs into live production systemsProgramming & FrameworksStrong proficiency in Python for backend development, data processing, and AI/ML integrationExperience with FastAPI (mandatory), Flask, or Django for API developmentBasic to intermediate understanding of Node.js for backend integration and collaborationBasic understanding of Flutter to support frontend integration of AI APIsFamiliarity with cURL for testing, debugging, and managing API requests and responsesMachine Learning & Deep LearningSolid understanding of machine learning and deep learning conceptsHands-on experience with frameworks such as TensorFlow, PyTorch, Keras, or scikit-learnExperience in NLP, neural networks, and modern AI architecturesAbility to train, validate, optimize, and deploy ML/DL modelsData & Database TechnologiesExperience with relational databases such as PostgreSQL or MySQLExperience with NoSQL and vector databases such as MongoDB, Pinecone, or OpenSearchKnowledge of data processing tools such as Pandas and NumPyFamiliarity with big data tools such as Spark or Hadoop (optional)Cloud & DevOpsExperience working with AWS cloud services including Lambda, S3, API Gateway, EC2, ECS/EKS, DynamoDB, and RDSKnowledge of Amazon Bedrock and SageMaker is preferredExperience with Docker and Kubernetes for containerization and orchestrationFamiliarity with CI/CD pipelines and DevOps practicesUnderstanding of IAM, VPC, encryption, and secure system designProfessional and Technical SkillsStrong understanding of microservices architecture and distributed systemsExpertise in API design, software architecture, and scalable system designStrong problem-solving, analytical thinking, and debugging skillsAbility to design, build, test, deploy, and operate AI-powered systems end-to-endExperience in performance optimization, scalability, latency, and cost trade-offsGood communication skills with the ability to explain complex technical concepts to cross-functional teamsAbility to assess existing processes, identify improvement areas, and suggest AI-driven solutionsAwareness of latest technologies and industry trendsGood to HaveExperience with advanced Agentic AI systems and workflow automationKnowledge of Graph RAG and knowledge graph-based retrieval systemsExperience in prompt optimization, LLM fine-tuning, and model evaluationExperience deploying AI/ML/GenAI solutions into production environmentsExposure to multiple cloud platforms such as AWS, Azure, or GCPFamiliarity with financial or enterprise domain systemsExperience with distributed systems, Snowflake, or large-scale data platformsSummaryThis role requires a strong foundation in Python backend development combined with hands-on experience in Generative AI, Machine Learning, and Deep Learning.The candidate should be capable of building scalable, production-ready AI systems, integrating advanced models, and enabling intelligent automation across enterprise workflows.